{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "plt.rcParams[\"font.sans-serif\"]=[\"SimHei\"] #设置字体\n",
    "plt.rcParams[\"axes.unicode_minus\"]=False #该语句解决图像中的“-”负号的乱码问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x52824e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year = [2019,2020,2021,2022]\n",
    "people = [10,50,80,200]\n",
    "plt.plot(year,people)\n",
    "plt.title(\"年度人数统计\")\n",
    "plt.xlabel(\"年度\")\n",
    "plt.ylabel(\"人数\")\n",
    "plt.fill_between(year, people, 0, color = 'green')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
